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• A breakdown of the location accuracy of the SALDN and ALDIS per year, indicating that rare large location errors still occur even as performance improves. • A methodology to fit statistical models to geographical lightning data and evaluate the quality of the fit. • The methodology is applied to lightning location system data time-correlated with ground-truth lightning events - tall tower lightning in both South Africa and Austria. • It is shown that the bivariate Students' t-distribution is a better statistical model for lightning location system errors than the bivariate Gaussian distribution. Lightning location systems geolocate lightning strokes. Given assumptions made in the geolocation models, errors in the reported locations can occur. Modelling these errors as a bivariate Gaussian distribution of historic stroke detections has found success in the form of confidence ellipses. However, the presence of outliers - strokes with large location errors - indicate that there is a better model for these errors. The Students' t-distribution is a "heavier" tailed distribution. This paper investigates whether the bivariate Students' t-distribution is a better model for such errors. A methodology for modelling and evaluating the distribution of location errors using maximum likelihood estimation, expectation-maximization and a Mahalanobis distance quality-of-fit test is described. This method is applied to stroke reports from the South African Lightning Detection Network and the Austrian Lightning Detection and Information System time-correlated with photographed lightning events to the Brixton Tower, South Africa and current measurements to the Gaisberg Tower, Austria respectively. In both cases, we find outliers in the distribution of location errors - even as the performance of the networks increase. Using the Mahalanobis test, we find the bivariate Students' t-distribution to be a better statistical model for both the South African and the Austrian events. [ABSTRACT FROM AUTHOR]